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Fernandes Sinaga
Institut Teknologi Sepuluh Nopember, Surabaya
Computer Science & IT

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Business Process Anomali Detection using Multi-Level Class Association Rule Learning Fernandes Sinaga; Riyanarto Sarno
IPTEK Journal of Proceedings Series No 1 (2015): 1st International Seminar on Science and Technology (ISST) 2015
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (149.413 KB) | DOI: 10.12962/j23546026.y2015i1.1135

Abstract

Recently, Business Process Management System (BPMS) is widely used by companies in order to manage their business process. The company’s business process has a possibility to have changes which can cause some variations of business process. These variations might be contain some anomalies. Any anomalies that can make some losses for the company can be regarded as a fraud. There were some research have done to detect anomalies in business process. But, there is some issues that still need improvement especially on the accuracy. This paper proposed Multi-Level Class Association Rule Learning method (ML-CARL) to detect business process anomalies accurately. This method is supported by the process mining method which is used to analyze the anomalies in process. From the experiment, ML-CARL method can detect anomalies with an accuracy of 0.99 and better than ARL method in previous research. It can be concluded that ML-CARL method can increase the accuracy of business process anomaly detection.
Co-Authors Riyanarto Sarno